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Journal articles on the topic 'Visual learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Sze, Daniel Y. "Visual Learning." Journal of Vascular and Interventional Radiology 32, no. 3 (2021): 331. http://dx.doi.org/10.1016/j.jvir.2021.01.265.

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2

Nida, Diini Fitrahtun, Muhyiatul Fadilah, Ardi Ardi, and Suci Fajrina. "CHARACTERISTICS OF VISUAL LITERACY-BASED BIOLOGY LEARNING MODULE VALIDITY ON PHOTOSYNTHESIS LEARNING MATERIALS." JURNAL PAJAR (Pendidikan dan Pengajaran) 7, no. 4 (2023): 785. http://dx.doi.org/10.33578/pjr.v7i4.9575.

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Visual literacy is the skill to interpret and give meaning to information in the form of images or visuals. Visual literacy is included in the list of 21st-century skills. The observation results indicate that most of the students have not mastered visual literacy well. One of the efforts that can be made to improve visual literacy is the provision of appropriate and right teaching materials. The research is an R&D (Research and Development) using a 4-D model, which is modified to 3-D (define, design, develop). The instruments used were content analysis sheets and validation questionnaires

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3

A Sowe, Ebou. "Momentum Contrast for Unsupervised Visual Representation Learning." Journal of Advances in Civil and Mechanical Engineering 2, no. 1 (2025): 01–06. https://doi.org/10.64030/3067-2457.02.01.02.

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This brief report presents a novel unsupervised learning representation learning method called momentum contrast. Momentum contrast uses a contrastive learning technique to learn representations by comparing features of related yet dissimilar images for efficient feature extraction and unsupervised representation learning. Similar images are grouped together, and dissimilar images are placed far apart. The method builds upon previous works in contrastive learning but includes a momentum optimisation step to improve representation learning performance and generate better quality representations

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4

Liu, Yan, Yang Liu, Shenghua Zhong, and Songtao Wu. "Implicit Visual Learning." ACM Transactions on Intelligent Systems and Technology 8, no. 2 (2017): 1–24. http://dx.doi.org/10.1145/2974024.

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5

Cruz, Rodrigo Santa, Basura Fernando, Anoop Cherian, and Stephen Gould. "Visual Permutation Learning." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 12 (2019): 3100–3114. http://dx.doi.org/10.1109/tpami.2018.2873701.

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6

Jones, Rachel. "Visual learning visualized." Nature Reviews Neuroscience 4, no. 1 (2003): 10. http://dx.doi.org/10.1038/nrn1014.

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7

Lu, Zhong-Lin, Tianmiao Hua, Chang-Bing Huang, Yifeng Zhou, and Barbara Anne Dosher. "Visual perceptual learning." Neurobiology of Learning and Memory 95, no. 2 (2011): 145–51. http://dx.doi.org/10.1016/j.nlm.2010.09.010.

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8

Richler, Jennifer J., and Thomas J. Palmeri. "Visual category learning." Wiley Interdisciplinary Reviews: Cognitive Science 5, no. 1 (2013): 75–94. http://dx.doi.org/10.1002/wcs.1268.

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9

Guinibert, Matthew. "Learn from your environment: A visual literacy learning model." Australasian Journal of Educational Technology 36, no. 4 (2020): 173–88. http://dx.doi.org/10.14742/ajet.5200.

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Based on the presupposition that visual literacy skills are not usually learned unaided by osmosis, but require targeted learning support, this article explores how everyday encounters with visuals can be leveraged as contingent learning opportunities. The author proposes that a learner’s environment can become a visual learning space if appropriate learning support is provided. This learning support may be delivered via the anytime and anywhere capabilities of mobile learning (m-learning), which facilitates peer learning in informal settings. The study propositioned a rhizomatic m-learning mo

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Taga, Tadashi, Kazuhito Yoshizaki, and Kimiko Kato. "Visual field difference in visual statistical learning." Proceedings of the Annual Convention of the Japanese Psychological Association 79 (September 22, 2015): 2EV—074–2EV—074. http://dx.doi.org/10.4992/pacjpa.79.0_2ev-074.

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11

Muslim, Fachruddiansyah, Ekawarna Ekawarna, Aminah Ramalia, Ricky Purnama Wirayuda, and Diki Chen. "Learning Intensity and Visual Learning Style on Learning Outcomes." Journal of Education Research and Evaluation 6, no. 2 (2022): 385–96. http://dx.doi.org/10.23887/jere.v6i2.40312.

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Learning activities do not have to be done for a long time. Good learning intensity is carried out regularly will make learning activities a habit. The learning process also needs to be supported by a learning style that suits the characteristics of students. This study aimed to analyze the intensity of learning-on-learning outcomes, visual learning styles on learning outcomes, and learning intensity and visual learning styles on learning outcomes. This type of research is quantitative with a descriptive quantitative approach. The sample used was 65 students with purposive sampling. Data colle

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12

Holland, Keith. "Visual skills for learning." Set: Research Information for Teachers, no. 2 (August 1, 1996): 1–4. http://dx.doi.org/10.18296/set.0900.

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13

MuhiAl-Din, Shaima, and Siddeeq Al - Bana. "Learning Visual Basic Reactively." TANMIYAT AL-RAFIDAIN 30, no. 92 (2008): 130–49. http://dx.doi.org/10.33899/tanra.2008.161729.

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14

Fuchs, R., J. Waser, and M. E. Groller. "Visual Human+Machine Learning." IEEE Transactions on Visualization and Computer Graphics 15, no. 6 (2009): 1327–34. http://dx.doi.org/10.1109/tvcg.2009.199.

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15

Turk-Browne, Nicholas B., Phillip J. Isola, Brian J. Scholl, and Teresa A. Treat. "Multidimensional visual statistical learning." Journal of Experimental Psychology: Learning, Memory, and Cognition 34, no. 2 (2008): 399–407. http://dx.doi.org/10.1037/0278-7393.34.2.399.

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16

Mountstephen, Mary. "SEN special: Visual learning." Primary Teacher Update 2011, no. 1 (2011): 38–39. http://dx.doi.org/10.12968/prtu.2011.1.1.38.

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17

Bischof, Walter F. "Visual Learning: An Overview." Swiss Journal of Psychology 63, no. 3 (2004): 151–64. http://dx.doi.org/10.1024/1421-0185.63.3.151.

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A review is presented of modern approaches to the learning and recognition of complex patterns, including discriminant functions, neural networks, decision trees, and hidden Markov models. Next, several relational learning systems are introduced and discussed, in detail one specific technique, conditional rule generation. This technique is shown to be very flexible and useful for the learning of static patterns, such as objects, as well as dynamic patterns, such as movement patterns. The technique is illustrated with a number of very difficult visual learning problems.

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18

Shams, L., A. Seitz, and V. van Wassenhove. "Audio-visual statistical learning." Journal of Vision 6, no. 6 (2010): 152. http://dx.doi.org/10.1167/6.6.152.

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19

Sanderson, Katharine. "Learning tools: Visual aids." Nature 477, no. 7366 (2011): 621–22. http://dx.doi.org/10.1038/nj7366-621a.

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20

Taylor, Sarah, Taehwan Kim, Yisong Yue, Ben Milner, and Iain Matthews. "Learning from visual speech." Journal of the Acoustical Society of America 140, no. 4 (2016): 3004. http://dx.doi.org/10.1121/1.4969315.

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21

Seitz, Aaron R., Robyn Kim, and Ladan Shams. "Sound Facilitates Visual Learning." Current Biology 16, no. 14 (2006): 1422–27. http://dx.doi.org/10.1016/j.cub.2006.05.048.

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22

Malec, James F., Robert J. Ivnik, and Nancy S. Hinkeldey. "Visual Spatial Learning Test." Psychological Assessment: A Journal of Consulting and Clinical Psychology 3, no. 1 (1991): 82–88. http://dx.doi.org/10.1037/1040-3590.3.1.82.

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23

Ji, Ruolei, and Lina J. Karam. "Learning-based Visual Compression." Foundations and Trends® in Computer Graphics and Vision 15, no. 1 (2023): 1–112. http://dx.doi.org/10.1561/0600000101.

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24

Yang, Chuanguang, Zhulin An, Linhang Cai, and Yongjun Xu. "Mutual Contrastive Learning for Visual Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 3045–53. http://dx.doi.org/10.1609/aaai.v36i3.20211.

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We present a collaborative learning method called Mutual Contrastive Learning (MCL) for general visual representation learning. The core idea of MCL is to perform mutual interaction and transfer of contrastive distributions among a cohort of networks. A crucial component of MCL is Interactive Contrastive Learning (ICL). Compared with vanilla contrastive learning, ICL can aggregate cross-network embedding information and maximize the lower bound to the mutual information between two networks. This enables each network to learn extra contrastive knowledge from others, leading to better feature r

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25

Hong, Richang, Yang Yang, Meng Wang, and Xian-Sheng Hua. "Learning Visual Semantic Relationships for Efficient Visual Retrieval." IEEE Transactions on Big Data 1, no. 4 (2015): 152–61. http://dx.doi.org/10.1109/tbdata.2016.2515640.

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26

S., Vimalananthi. "LEARNING VISUAL SEMANTIC RELATIONSHIPS FOR PROFICIENT VISUAL RECOVERY." International Journal of Computational Research and Development 2, no. 1 (2017): 51–54. https://doi.org/10.5281/zenodo.546016.

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In this paper, I investigate how to establish the relationship between semantic concepts based on the large-scale real-world click data from image commercial engine, which is a challenging topic because the click data suffers from the noise such as typos, the same concept with different queries, etc. I first define five specific relationships between concepts. I extract some concept relationship features in textual and visual domain to train the concept relationship models. The relationship of each pair of concepts will thus be classified into one of the five special relationships. I study the

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27

OGUNRINBOKUN, Bamidele Emmanuel, Omotayo Matina OLUWOLE-MOSES, and Temiloluwa Oluwatunmise BADEJO. "Impact of Learning Styles on Academic Performance of Accounting Education Students at Federal College of Education (Technical) Akoka." FUO-Journal of Educational Research 4, no. 2 (2025): 51–61. https://doi.org/10.5281/zenodo.14885773.

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AbstractThis study explores the relationship between learning styles and academic performance among 300-level Accounting Education students at the Federal College of Education (Technical) Akoka. Employing a descriptive survey design, the study examines dominant learning styles—visual, auditory, kinesthetic, and a combination—to determine their influence on academic success. Data collection involved a validated learning style inventory and semester performance records, analyzed using descriptive and inferential statistics. Results revealed that visual learning is the most dominant s

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28

HP, Bambang Setiyo, Hartati Mochtar, and Atwi Suparman. "The Effect of Blended Learning Approach and Visual-Spatial Ability on Learning Outcomes." JETL (Journal of Education, Teaching and Learning) 5, no. 1 (2020): 193. http://dx.doi.org/10.26737/jetl.v5i1.1150.

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The purpose of this study was to conduct an empirical study to find out the aspects that influence the basic CNC learning outcomes, in this case regarding the application of the approach blended learning, and the abilities visual-spatial possessed by students who take the course. Based on the types of research variables that exist, then this experimental research is appropriate to be carried out using the experimental Treatment by Level design. Data analysis in this experimental study used 2-way ANOVA with one treatment variable and one attribute variable. This research was carried out using e

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29

Aji, Daru Tunggul. "Literasi Visual sebagai Pendekatan dalam Pembelajaran Fotografi." Rekam 17, no. 2 (2021): 123–34. http://dx.doi.org/10.24821/rekam.v17i2.5660.

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Visual Literacy As an Approach To Learning Photography. This article is an overview of the current photographic phenomena. Visual literacy as an approach becomes an offer in the development of photography learning science. As a of discipline, photography has the complexity of learning, just like other scientific disciplines. In photography learning, visual literacy is a significant capital. Visual literacy can be understood as a person's ability to respond to phenomena. It's not just the ability to switch media (design); from the oral to the visual, from the textual to the visual, from the aud

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30

Gole, Sule. "Museums as Visual Laboratories in the Learning-Teaching Process." New Trends and Issues Proceedings on Humanities and Social Sciences 2, no. 7 (2016): 48–53. http://dx.doi.org/10.18844/gjhss.v2i7.1179.

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31

Macintyre, Ronald. "Rich Pictures: Non Visual use of Visual Tools." International Journal of Management and Applied Research 7, no. 3 (2020): 360–69. https://doi.org/10.18646/2056.73.20-026.

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This reflective article explores the use of Soft Systems Methodology (SSM) Rich Pictures within the learning design. It introduces SSM as part of a broader interest in systems within education and positions SSM as part of a broader family of participatory approaches within a range of fields. Based on learning design practice with organisations looking to (re)connect with learners, the paper sets out a scenario where participants are asked to draw of a Rich Picture of the “ideal learner”, where questions of future selves and ideal forms highlight are used to highlight the non-repres

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32

Sahuni, Sahuni, Iffah Budiningsih, and Lisna Marwani P. "INTERACTION OF LEARNING MEDIA WITH LEARNING INTEREST IN ARABIC LEARNING OUTCOMES." Akademika 9, no. 02 (2020): 43–52. http://dx.doi.org/10.34005/akademika.v9i02.871.

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The research aims to determine the influence of visual media, print media and the interest in learning outcomes in Arabic. The research method used is the method of experiment with sample two classes, which amounted to 39 students. Research samples were taken in a simple randomized basis. The Data was analyzed in descriptive and ANAVA. The results are: a) the learning outcome of Arabic students who are taught using visual media is higher than using print media; b) there is interaction between the media and learning interest in Arabic language learning outcomes; c) on students who have a high l

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33

Sacha, Dominik, Matthias Kraus, Daniel A. Keim, and Min Chen. "VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning." IEEE Transactions on Visualization and Computer Graphics 25, no. 1 (2019): 385–95. http://dx.doi.org/10.1109/tvcg.2018.2864838.

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34

Haniah, Haniah, and Jumadil Jumadil. "Visual Technology Development for Arabic Learning Based on ADDIE Development Model." Jurnal Al-Maqayis 9, no. 2 (2022): 147. http://dx.doi.org/10.18592/jams.v9i2.5692.

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This article aimed to determine the variety of visual technology and its development in learning Arabic. Visual technologies that could be used by Arabic language teachers were all kinds of media that could be used in general learning, both in the form of non-projected visual media and projected visual media, Non-projected visuals in the form of graphic media, realia (real objects), and models, while visual media that could be projected are LCDs, and digital cameras. In its development, Arabic learning could be developed with various models, one of which is the development of the ADDIE (analyz

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35

Wilson, Jay, and Vince Bruni-Bossio. "Using Visual Mapping to Communicate Connections Between Learning Outcomes and Student Tasks." International Journal of Designs for Learning 11, no. 3 (2020): 13–22. http://dx.doi.org/10.14434/ijdl.v11i3.26101.

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Visual mapping is a method of presenting course material in a visual format to aid comprehension. This paper looks at applications of visual mapping in post-secondary courses to engage university students more deeply from the very beginning of a course, through creating visuals to which students can be constantly referred. We discuss our efforts to design visuals of learning outcomes, objectives, concepts, and processes in multiple courses. We will highlight the process we used to build the graphics and keyways these graphics improve the communication and understanding of learning outcomes, co

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36

Maulida, Sarah, and Muhamad Sofian Hadi. "Using Audio Visual Media to Improve English Learning Outcomes." Jurnal Studi Guru dan Pembelajaran 5, no. 1 (2022): 11–15. http://dx.doi.org/10.30605/jsgp.5.1.2022.1297.

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Video recordings, slides, and sound are examples of audio-visual media. It is thought that learning to use audio visuals in English lessons will help students better understand the problems or lessons presented. Because listeners are encouraged to use their imagination and optimize their left and right brain function. Audio-visual media in the form of animated learning videos and power points are used in English subjects at SMK Grafika that are conducted online or online. The purpose of this study was to see if audio visuals could boost students' motivation and enthusiasm for learning during o

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37

Vogels, Rufin. "Mechanisms of Visual Perceptual Learning in Macaque Visual Cortex." Topics in Cognitive Science 2, no. 2 (2010): 239–50. http://dx.doi.org/10.1111/j.1756-8765.2009.01051.x.

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38

Nagata, Takeshi, and Daiki Hashimoto. "Visual Inspection by Deep Learning and Machine Learning." Journal of The Japan Institute of Electronics Packaging 23, no. 4 (2020): 271–74. http://dx.doi.org/10.5104/jiep.23.271.

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39

Haider, Hilde, Katharina Eberhardt, Alexander Kunde, and Michael Rose. "Implicit visual learning and the expression of learning." Consciousness and Cognition 22, no. 1 (2013): 82–98. http://dx.doi.org/10.1016/j.concog.2012.11.003.

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40

Sithole, Seedwell, Ragini Datt, Paul de Lange, and Meredith Tharapos. "Learning accounting through visual representations." Accounting Research Journal 34, no. 4 (2021): 365–84. http://dx.doi.org/10.1108/arj-06-2018-0100.

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Purpose The purpose of this study is to investigate the effectiveness of diagrammatic visualisation techniques versus sentential learning contexts in an accounting subject using the theoretical lens of cognitive load theory (CLT). Design/methodology/approach The present study used four groups of students; two groups completed a task using diagrammatic visualisation learning materials, with one of the groups undertaking their leaning activities collaboratively and another on an individual basis, whereas two comparison groups were given a sentential learning context without diagrams, with one gr

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41

Hout, M. C., and S. D. Goldinger. "Learning in repeated visual search." Attention, Perception & Psychophysics 72, no. 5 (2010): 1267–82. http://dx.doi.org/10.3758/app.72.5.1267.

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42

Copperman, Elana, Catriel Beeri, and Nava Ben‐Zvi. "Visual modelling of learning processes." Innovations in Education and Teaching International 44, no. 3 (2007): 257–72. http://dx.doi.org/10.1080/14703290701486571.

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43

Dosher, Barbara, and Zhong-Lin Lu. "Visual Perceptual Learning and Models." Annual Review of Vision Science 3, no. 1 (2017): 343–63. http://dx.doi.org/10.1146/annurev-vision-102016-061249.

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44

Laamerad, Pooya, Daniel Guitton, and Christopher C. Pack. "Eye movements shape visual learning." Proceedings of the National Academy of Sciences 117, no. 14 (2020): 8203–11. http://dx.doi.org/10.1073/pnas.1913851117.

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Most people easily learn to recognize new faces and places, and with more extensive practice they can become experts at visual tasks as complex as radiological diagnosis and action video games. Such perceptual plasticity has been thoroughly studied in the context of training paradigms that require constant fixation. In contrast, when observers learn under more natural conditions, they make frequent saccadic eye movements. Here we show that such eye movements can play an important role in visual learning. Observers performed a task in which they executed a saccade while discriminating the motio

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45

Chetverikov, Andrey, Gianluca Campana, and Árni Kristjánsson. "Rapid learning of visual ensembles." Journal of Vision 17, no. 2 (2017): 21. http://dx.doi.org/10.1167/17.2.21.

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46

Turk-Browne, Nicholas. "Hippocampal contributions to visual learning." Journal of Vision 18, no. 10 (2018): 1365. http://dx.doi.org/10.1167/18.10.1365.

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47

Koller, Harold P. "Visual processing and learning disorders." Current Opinion in Ophthalmology 23, no. 5 (2012): 377–83. http://dx.doi.org/10.1097/icu.0b013e32835720e2.

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48

Beymer, D., and T. Poggio. "Image Representations for Visual Learning." Science 272, no. 5270 (1996): 1905–9. http://dx.doi.org/10.1126/science.272.5270.1905.

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49

Hiles, B. P., N. Intrator, and S. Edelman. "Unsupervised learning of visual structure." Journal of Vision 2, no. 7 (2010): 74. http://dx.doi.org/10.1167/2.7.74.

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50

Li, Jia. "Learning-based visual saliency computation." ACM SIGMultimedia Records 2, no. 4 (2010): 8–9. http://dx.doi.org/10.1145/2039331.2039336.

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